<i>Drosophila</i> Sociality Influences Immune Peptide Load and Apoptosis-induced Tumor Suppression independently of the Antitumor Peptide Defensin
Bibliographic record
Abstract
SUMMARY Patient’s social environment might influence cancer outcome. This potentially happens in Drosophila , as we previously reported that the social context influences the growth of intestinal tumors. To uncover the underlying social-induced physiological mechanisms, we performed RNA-seq of isogenized beheaded tumorous Drosophila . Importantly, expression of several immune peptides varied according to the social-induced tumor growth effect. Furthermore, ectopic expression in tumors of the apoptotic-inhibitor p35 suppressed the social-induced effect. Next, we challenged the immune peptide Defensin, previously reported to suppress imaginal disc tumor growth through a cell-death/JNK-dependent network. Nonetheless, the social-induced tumor suppression was maintained upon Defensin overexpression or JNKK-knockdown in tumors, and in defensin mutants. Surprisingly, tumor growth was reduced in the latter, indicating that Defensin sustains the growth of these intestinal tumors. In summary, our study indicates that the social context affects the immune response and that a given immune peptide may have opposite effects depending on tumor type.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".